A Laboratory Study of Bayesian Updating in Small Feedback-Based Decision Problems

This study explores small feedback-based decision problems experimentally. Conducted were the experiments in which the decision-maker’s payoff distribution was limited to either favorable distribution or unfavorable distribution. The first remarkable observation revealed complexity/loss aversion in the experiment. The second observation included the law of small numbers. Deviations from maximization were also observed. Finally, we investigated the imperfect Bayesian decision-makers observed in the experiment by exploring to what extent the decision-makers could update subjective Bayesian probability and rely on it in making decisions.